Diffusion weighted imaging (DWI) has evolved substantially over its lifetime, encompassing a range of related techniques seeking to better resolve and image diffusion processes in vivo. New sequences and sampling methods now permit resolution of details beyond the determination of the diffusion tensor. Modelling approaches like CHARMED [1]} and NODDI [2]} have motivated MRI protocols with the power to efficiently resolve major microstructural properties across a wide range of tissues. Novel techniques require arbitrary gradient waveform trajectories for multiple purposes, including tensor-valued diffusion encoding, more specific feature encoding (such as flow or exchange), as well as for combatting certain artifacts (e.g. Maxwell, background field effects etc.). As well, compared to single diffusion encoding, double [3]} and multiple diffusion encoding methods (SDE, DDE and MDE respectively [4]}) offer a way to disentangle several microscopic diffusion phenomena that may be collated in an SDE experiment [5]}, [6]}, resolve diffusion at small time scales [7]}, [8]}, [9]}, amongst other possibilities offered by this technique.
